Combined impact of lipidomic and genetic aberrations on clinical outcomes in metastatic castration-resistant prostate cancer

Background Both changes in circulating lipids represented by a validated poor prognostic 3-lipid signature (3LS) and somatic tumour genetic aberrations are individually associated with worse clinical outcomes in men with metastatic castration-resistant prostate cancer (mCRPC). A key question is how the lipid environment and the cancer genome are interrelated in order to exploit this therapeutically. We assessed the association between the poor prognostic 3-lipid signature (3LS), somatic genetic aberrations and clinical outcomes in mCRPC. Methods We performed plasma lipidomic analysis and cell-free DNA (cfDNA) sequencing on 106 men with mCRPC commencing docetaxel, cabazitaxel, abiraterone or enzalutamide (discovery cohort) and 94 men with mCRPC commencing docetaxel (validation cohort). Differences in lipid levels between men ± somatic genetic aberrations were assessed with t-tests. Associations between the 3LS and genetic aberrations with overall survival (OS) were examined using Kaplan-Meier methods and Cox proportional hazard models. Results The 3LS was associated with shorter OS in the discovery (hazard ratio [HR] 2.15, 95% confidence interval [CI] 1.4-3.3, p < 0.001) and validation cohorts (HR 2.32, 95% CI 1.59–3.38, p < 0.001). Elevated plasma sphingolipids were associated with AR, TP53, RB1 and PI3K aberrations (p < 0.05). Men with both the 3LS and aberrations in AR, TP53, RB1 or PI3K had shorter OS than men with neither in both cohorts (p ≤ 0.001). The presence of 3LS and/or genetic aberration was independently associated with shorter OS for men with AR, TP53, RB1 and PI3K aberrations (p < 0.02). Furthermore, aggressive-variant prostate cancer (AVPC), defined as 2 or more aberrations in TP53, RB1 and/or PTEN, was associated with elevated sphingolipids. The combination of AVPC and 3LS predicted for a median survival of ~12 months. The relatively small sample size of the cohorts limits clinical applicability and warrants future studies. Conclusions Elevated circulating sphingolipids were associated with AR, TP53, RB1, PI3K and AVPC aberrations in mCRPC, and the combination of lipid and genetic abnormalities conferred a worse prognosis. These findings suggest that certain genotypes in mCRPC may benefit from metabolic therapies. Supplementary Information The online version contains supplementary material available at 10.1186/s12916-022-02298-0.


Table of Contents
.1: Sphingolipids with significantly elevated levels in men with any AR aberration in the discovery cohort compared to men without, and their fold-change in the validation cohort. ........ 10 Table S6.2: Sphingolipids with significantly elevated levels in men with any TP53 aberration in the discovery cohort compared to men without, and their fold-change in the validation cohort. ........ 11 Table S6.3: Sphingolipids with significantly elevated levels in men with RB1 deletion in the discovery cohort compared to men without, and their fold-change in the validation cohort. ........ 12 Table S6.4: Sphingolipids with significantly elevated levels in men with any PI3K pathway aberration in the discovery cohort compared to men without, and their fold-change in the validation cohort. .
.. 23 S1 Detailed methods for lipidomic analysis S1.1 Blood collection and lipid extraction Peripheral blood from patients was sampled and lipid extraction performed as previously described. Whole blood was collected into 10mL EDTA-containing tubes and two-step centrifugation performed to separate plasma and buffy coat. The centrifugation speed differed between the two cohorts. For the discovery cohort, blood samples were first centrifuged at 1600 x g for 15 minutes and the supernatant transferred to a fresh tube, where it was centrifuged again at 5000 x g for 10 min. For the validation cohort, both centrifugation speeds were 3000 rpm for 10 minutes. Aliquots of the plasma after the second centrifugation were stored at -80 o C until required.

S1.2 Quality control samples
Replicates of two types of quality controls (QC) were extracted and ran together with the study plasma samples: • Pooled human plasma from healthy individuals (PQC) • National Institute of Standards and Technology human plasma standard reference material 1950 (NIST1950). This was developed by NIST from a collaboration with the National Institute of Health (NIH), and the National Institute of Diabetes and Digestive and Kidney Disease (NIDDK), to allow comparisons between data sets run within the same laboratory and with other laboratories globally.
The coefficient of variation (%CV) of the lipid levels in these QC samples passed the required threshold of mean %CV <15% and median %CV <10%.

S1.3 Liquid chromatography-mass spectrometry analysis
Liquid chromatography-mass spectrometry (LC-MS) analysis was performed as previously described. Lipid extracts were analysed on an Agilent 6490 QQQ mass spectrometer with an Agilent 1290 series HPLC system. Samples from the validation cohort were analysed with other samples not part of this study as 3 batch runs, where the batch differences were adjusted by median centering with PQC samples. The median concentration of each lipid species of the PQC samples in the 3 batches was first calculated, and then used to derive a median center value for each lipid species (median center value = median of batch 1 / median of batch 1, 2 or 3). The median center value of each batch was then multiplied to the concentration of lipids in the study samples, resulting in the alignment of all batches to the first batch.

S1.4 Data normalisation
Normalisation is a data pre-processing step that is essential for large scale analyses of multi-variable data (e.g. genomic, proteomic). This normalisation step adjusts for biases that can arise from sample preparation (e.g. sample loss, evaporation, irregular extraction efficiency, pipetting errors), biological effects (e.g. differences in water content) or biological variation (e.g. differences in individuals unrelated to disease pathology).
The lipidomic datasets from both cohorts were normalised independently according to the Probabilistic Quotient (PBQ) normalization method as previously described, and adapted from Dieterle et al (2006). The reference sample used in PBQ normalization was created from the mean levels of each lipid species across all the plasma samples of each cohort respectively. Final values are logarithm-2 of pmol/mL.

S1.5 Dataset alignment and calculation of the 3-lipid signature
To determine the presence of the circulating 3LS of poor prognosis, the lipidomic data was first aligned to that of the original cohort from which the 3LS was derived using the ComBat algorithm (R package sva, v3.34.0) to remove batch differences, as the lipidomic datasets were produced by different LC-MS instruments and on a different occasion. Next, the 3LS status of each patient was calculated from a logistic regression model consisting of ceramide Cer(d18:1/24:1), sphingomyelin SM(d18:2/16:0) and phosphatidycholine PC (

Blood collection and cfDNA extraction
Peripheral blood was sampled and cell-free DNA (cfDNA) was isolated as published previously. The process of blood collection was described in S1.1. Samples were then stored at -80 o C until required for batch processing. Up to 5mL of plasma was used to extract cfDNA using the QIAamp circulating nucleic acid kit (Qiagen, Hilden, Germany), with large genomic fragments removed with AMPure XP beads (Beckman Coulter, Brea, CA, USA). DNA was quantified (Qubit 2.0 fluorometer, ThermoFisher Scientific, Waltham, Massachusetts, USA) and underwent quality assessment (Bioanalyzer 2100, Agilent Technologies, California, USA).

S2.2 Targeted capture, sequencing and bioinformatics
Library preparation, hybrid-capture and sequencing Up to 40ng of extracted cfDNA was used for preparation of next-generation sequencing (NGS) libraries, a process which has been described in detail previously. Amplified DNA libraries underwent further quality assessment (Bioanalyzer 2100) and then were subsequently hybridised overnight to a targeted panel capturing exonic regions from 90-120 genes (Table S1.2.1). Captured fragments were further PCR amplified and underwent a final quality assessment (Bioanalyzer 2100), before being sequenced on the Illumina HiSeq X Ten.

Somatic mutation identification
Consensus binary alignment map (BAM) files were derived as previously described to reduce sequencing and PCR errors. Candidate somatic variants were identified using an in-house pipeline. A variant was considered a candidate somatic mutation when all 3 criteria were met: 1) presence of at least 4 distinct fragments contained the mutation, 2) variant allelic frequency (AF) was at least 0.25%, or 0.1% for hotspot loci (as defined by COSMIC and http://www.cancerhotspots.org), and 3) variant does not appear in public databases of common germline variants (1000 genomes, ExAC, gnomAD and KAVIAR). Candidate somatic mutations were further filtered to include missense, nonsense, frameshift or splice site alterations, and to exclude benign variants and haematopoietic expansion-related variants.

Estimation of ctDNA fraction
Circulating tumour DNA (ctDNA) was estimated based on the allele fractions of autosomal somatic mutations, using a method described previously. ctDNA fraction was dichotomized to above or below 2% for multivariable analyses, an approach that has been used in other studies involving patients with advanced prostate cancer due to challenges in accurately calculating ctDNA fraction in samples with low ctDNA abundance.

Targeted copy number analysis
Estimation of panel-based copy number variation (CNV) has been described in greater detail previously. Briefly, in-house algorithms calculated the on-target unique fragment coverage using the consensus BAM file. The fragment was corrected for GC bias, then compared against corresponding coverage from a group of normal reference samples to estimate the significance of the copy number variant. Gains or deletions with an absolute z-score >3 and absolute CNV change above minimum gain/deletion thresholds were considered true events.

S2.3 Analytical validation of cfDNA assay CNV detection
Details of analytical validation of sensitivity, specificity and precision of the CNV detection assay in cfDNA has been described in a previous study.

S6 Fold difference in lipid levels between men with and without genetic aberrations, as assessed with t-tests
The tables below show the fold differences of the sphingolipid levels and P-values from the t-test comparisons. The different sphingolipid isoforms have varying concentration ranges, which are displayed in Section S10.        S8 Cox proportional hazards analysis of overall survival based on the aggressive-variant prostate cancer and 3-lipid signature combination    S10 Plasma concentrations of sphingolipids in the discovery and validation cohorts.
Sphingolipids are lipids with a sphingoid base of which the d18:1 isoform is the major isoform present in plasma.